| At present,China is in the stage of rapid urbanization development.As of 2021,China’s urbanization rate has reached 63.89%.The continuous expansion of cities has caused a series of social problems.Among them,the phenomenon of local"population hollowing" caused by the influx of a large number of young and middle-aged laborers from rural areas to cities to make a living has attracted the attention of academic circles.The migration of a large number of rural populations has led to significant changes in the number and structure of the local population.The number of rural populations has dropped sharply,the population is aging and children are aging,and problems such as land abandonment are prominent.China has achieved the goal of poverty alleviation by 2021.On February 21,2021,the "Opinions of the Central Committee of the Communist Party of China and the State Council on Comprehensively Promoting Rural Revitalization and Accelerating Agricultural and Rural Modernization" was released.Obviously,population has become an important guarantee for consolidating the achievements of poverty alleviation and realizing rural revitalization.However,the ever-increasing population hollowing has brought great challenges to the realization of rural revitalization.Therefore,the study of population hollowing has important theoretical and practical value.Identifying the temporal and spatial distribution characteristics of population hollowing is the premise and necessary condition to solve the problem of population hollowing.Although previous studies on population hollowing have made some progress,there are still bottlenecks:1)Population census and statistical data have poor current status,low spatial and temporal resolution,and slow updating,making it difficult to reveal the heterogeneity of population spatial and temporal distribution within administrative divisions;2)The spatiotemporal dynamic characteristics of population hollowing are unclear,and the coverage of existing studies is low,which makes it difficult to meet the needs of local identification of population hollowing characteristics;3)The driving mechanism of population hollowing is unclear,and it is difficult to clarify the motivation of population hollowing.The central region is an important energy base,transportation hub,agricultural product base and labor export base in China.In order to revitalize the central region,the Central Committee of the Communist Party of China and the State Council issued "Several Opinions on Promoting the Rise of the Central Region" in 2006.However,the results of the seventh national census released on May 11,2021 showed that the central region accounted for 25.83%of the population,a decrease of 0.79 percentage points compared with 2010.Population hollowing poses a serious challenge to the rise of the central region.Based on this,this paper selects the six central provinces as the research area,takes the degree of population hollowing as the research object,and uses the random forest model to estimate the degree of population hollowing based on nighttime light remote sensing images,social perception data and existing data,and the production population hollowing.Degree map data product;based on standard error ellipse and slope model to study the spatiotemporal dynamics of population hollowing degree in six central provinces;establish a quantitative relationship model between influencing factors and population hollowing degree,and conduct research on the driving mechanism of population hollowing based on geographic detectors.The research results can provide basic data support for the realization of rural revitalization,and have important scientific significance and application value.The main contents are as follows:(1)In this paper,the entropy weight method is used to construct an index of the degree of population hollowing,and the potential population hollowing area is identified in the study area.Finally,the random forest model,the geographically weighted regression model and the multiple linear regression model are used to simulate the population hollowing degree.After screening,a total of 6,742 potential population hollowing areas were retained,and the results showed that most of them were concentrated in rural areas;the proportion of potential hollowing townships in each province to the total number of townships was ranked as follows:Jiangxi Province(86%)>Hunan Province(84%)>Hubei Province Province(72%)>Anhui Province(68%)>Shanxi Province(63%)>Henan Province(61%),in terms of quantity,Jiangxi Province has the largest number of potential population hollow areas,and Henan Province has the largest number of potential hollow areas The number is the least;the results of multi-model population hollowing modeling and validation show that:random forest model accuracy(R_c~2=0.6152,R_v~2=0.595)>geographic weighted regression model accuracy(R_c~2=0.4279,R_v~2=0.386)>multiple linear regression model accuracy(R_c~2=0.1041,R_v~2=0.0729).Therefore,the random forest model is the best model simulated by the population hollowing model.(2)This paper uses the population hollowing index to obtain the population hollowing distribution map at the township scale,and uses the best model to obtain the population hollowing distribution map at the grid scale.The population hollowing of the six central provinces shows the distribution of "high in the north and low in the south",and the grid-scale distribution map can better show the spatial heterogeneity within the region;the hollowing of the population from 2016 to 2020 shows the distribution of "falling in the north and rising in the south" As well as the trend of population hollowing "center of gravity moving south",its distribution shows a high degree of spatial autocorrelation.Finally,through analysis,it is found that the distribution and degree of population hollowing in the six central provinces may be affected by physical geography,social economy and national policies.(3)This paper studies the driving mechanism of population hollowing in six central provinces based on the geographic detector method.The factor detection results show that the population hollowing in the six central provinces is mainly affected by natural factors(in which NDVI has the strongest explanatory power for the dependent variable),followed by meteorological factors and air pollution variables;the interaction detection results show that the interaction between the two factors must be greater than that of a single factor.Consistent with the factor detection results,the interaction between natural factors has the greatest impact on population hollowing in the six central provinces(in which the interaction between NDVI and each variable has the strongest explanatory power),followed by meteorological factors and the relationship between air pollution variables and various factors.Interactions;location traffic and socioeconomic factors have no significant effect on population hollowing in terms of the explanatory power of a single factor or the interaction with other variables. |
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